Creating a linearized data structure for ordering images based on their attributes

ABSTRACT

A system and method is provided for creating a linearized data structure for ordering a set of images based on their attributes. The system and method includes characterizing each of a set of images based on the attributes of the images, mapping the set of images onto a curve based on the attributes of the images, and generating an ordered arrangement of the images by traversing the curve. The ordered set of images may be used for displaying the images for efficient viewing and browsing.

This application is a divisional of prior U.S. patent application Ser. No. 09/150,496, now U.S. Pat. No. 6,233,367, filed on Sep. 9, 1998. This application is also related to copending U.S. patent application Ser. No. 09/777,164, filed on Feb. 5, 2001.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The described invention relates to the field of data management. In particular, the invention relates to categorizing and ordering images based on their attributes.

2. Description of Related Art

Browsing is a technique that users employ to view information rapidly for deciding whether the content is relevant to their needs. Browsing should give the user quick access to view the content and focus on sections of further interest. Multiple images are typically displayed. The user is able to scroll through the images until the desired image is found.

It is difficult, however, to characterize and sort images based on attributes of the images. While certain images may be closely related by one attribute, they may not be closely related by other attributes. Additionally, if many images are stored in a data structure, the amount of time to either place an image into the data structure or retrieve an image from the data structure can become excessive, such that a user will not want to wait.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows one embodiment of a flowchart of the process of creating dual linearizations for each image.

FIG. 2 shows an example of a space-filling curve.

FIG. 3 shows an embodiment in which a second curve is employed to improve the reliability of providing images that are close together in the multi-dimensional space.

FIG. 4 shows an example of a browser interface based on a dual linearization.

FIG. 5 shows an example of a browser interface based on a triple linearization.

FIG. 6 shows an example of a system implemented on an SQL server that communicates with a middleware server to construct and serve web pages to an end user.

FIG. 7 shows the 1st, 2nd, 3rd and 4th orders of a two-dimensional Hilbert curve.

FIG. 8 shows the 1st, 2nd, and 3rd orders of a two-dimensional Peano curve.

DETAILED DESCRIPTION

A method of characterizing images that scales well for large amounts of images is described. Due to the large number of attributes by which an image may be categorized, up until this disclosure, it has been difficult to categorize and display like images in an efficient manner. The present invention provides for display of images based on multiple linearizations of the images, such as a first linearization and a second linearization. “Linearization,” in this application, implies not just an ordering of a set of points, but also storing their positions, or at least relative distances between their positions. In one embodiment, the linearizations are performed by traversing two space-filling curves. In another embodiment, the linearizations are performed by traversing a cluster data structure. More than two linearizations may be displayed.

The method described herein is scaleable, so that the amount of time to categorize and display a portion of a set of images does not change significantly whether the set of images is relatively small (e.g., a few hundred images) or relatively large (e.g., greater than 50,000 images).

Two effective methods for linearizing the images are described: a space-filling curve method and a clustering method. Other methods, however, can also be used to linearize the images.

Space Filling Curves

An image may be categorized by various attributes, or components, of the image. For example, an image can be broken down and characterized by color information, shape information, size information, texture information, objects represented, or based on other content of the image. The image may also be categorized by tag information, which is information associated with each image, but which may not be part of the image itself. Tag information may include annotation information, audio information, time stamp information, global position information (GPS), and so forth.

In one example, a histogram of an image is generated using 600 different color bins. The histogram includes the number of pixels corresponding to each of the 600 colors used.

In one embodiment, each of the components of the histogram is used to map the image to a coordinate in N-dimensional space. For this example, N is 600. Image 1, for example, is mapped to the coordinate:

<attribute1, attribute2, attribute3, . . . attribute N>

in N-dimensional space, where attribute 1 is the number of pixels corresponding to a first color, attribute 2 is the number of pixels corresponding to a second color, and so forth.

Multiple images may be mapped into the N-dimensional space. Images that are located close together in the N-dimensional space are closely related by their attributes. In contrast, those that are located farther apart in the N-dimensional space are less closely related by their attributes.

One method that has been used for ordering a set of images mapped into N-dimensional space is by mapping all of the images to an arbitrary line. The position on the line to which the images are mapped indicates the linearization of the images. This linearization, however, does not preserve the relationship of similarly attributed images. This is because the mapping of the N-dimensional space into a one dimension line does not maintain information about how closely images are related in the N-dimensional space.

One embodiment of the present invention linearizes the images using a space filling curve to help maintain the relationship of like images. A space-filling curve is a continuous path that visits every point in a k-dimensional space. The space-filling curve provides a way to linearize the points in the multi-dimensional space.

Space-filling curves have been studied for many years, and more recently have interested computer scientists. For example, see “Fractals for Secondary Key Retrieval” by C. Faloutsos and S. Roseman (Technical Report CS-TR-2242, University of Maryland Institute for Advanced Computer Studies, May 1989) for an example of the use of space-filling curves. There are several different types of space filling curves including the Hilbert curve, the Peano curve, the Sierpinski curve, the Lebesgue curve, and the Schoenberg curve. See Hans Sagan, “Space-Filling Curves,” Springer-Verlag, 1994, for more information on space-filling curves.

FIG. 7 shows the 1st, 2nd, 3rd and 4th orders of a two-dimensional Hilbert curve. The procedure to derive higher orders of the Hilbert curve is to rotate and reflect the curve at vertex 0 and at vertex 3. The curve can keep growing recursively by following the same rotation and reflection pattern at each vertex of the curve.

FIG. 8 shows the 1st, 2nd, and 3rd orders of a two-dimensional Peano curve. The procedure to derive higher orders of the Peano curve is to replace each vertex of the basic curve with the previous order curve. The Hilbert curve and Peano curves can be extended into multiple dimensions, as is well-known in the art.

Space filling curves have the property that the points that are close to each other on the space-filling curve are also close to each other in the multi-dimensional space. However, the opposite is not true: the points that are close to each other in the multi-dimensional space are not necessarily close together on the space-filling curve.

FIG. 2 shows an example of a two-dimensional and three-dimensional space-filling curve. In this case, an N-dimensional Hilbert curve is employed. Two points 10 and 11 that are close to each other in the multi-dimensional space lie close to a partition boundary separating two sections of the multi-dimensional space. When the curve is traversed as shown at the bottom of FIG. 2, the points that were close in space map to points that lie far apart in linearization. This undesirable distancing of close points occurs at, and because of, partition boundaries of the Hilbert and Peano curves.

FIG. 3 shows an embodiment in which a second curve is employed to improve the reliability of providing images that are close together in the multi-dimensional space. In one embodiment, a Peano curve is used to complement a Hilbert curve. The Peano and Hilbert curves are constructed such that their partition boundaries, where undesirable results are likely to happen, occur in different locations. Points 20 and 21 that lie close in space will likely end up close together in either the Hilbert curve or the Peano curve, as shown in FIG. 3.

Table 1 shows an example of a computer program written in C language that maps a point in N-dimensional space to a position on a one-dimensional traversal of a Hilbert curve and a position on a one-dimensional traversal of a Peano curve. In one embodiment, the code maps the point in N-dimensional space to an interval between 0 and 1. The output of the program is a bit pattern identifying a number between 0 and 1 for each image being analyzed. As other images are converted into other bit patterns between 0 and 1, a database is set up with a first linearization based upon the Hilbert curve and a second linearization based upon the Peano curve.

In one embodiment, for simplification, images that map to a partition boundary are adjusted slightly so that they do not fall directly on the partition boundary. This is done because a curve may hit points on a partition boundary multiple times. In a Hilbert curve, the partition boundaries are at points that are any multiple of ½, ¼, ⅛, {fraction (1/16)}, etc, of the N-dimensional space. In a Peano curve, the partition boundaries are at points that are any multiple of ⅓, {fraction (1/9)}, {fraction (1/27)}, etc., of the N-dimensional space.

TABLE 1 /*********************************************************** © Intel Corporation, 1998 Pseudo-code for collapsing points in a space to points in a line, via space-filling curve traversal. The “points” are arrays of plain old integer coordinates. These are combined to produce a single value, indicating a position along a line. Since we're dealing with high-dimensional spaces (DIMENSION == ˜600), the final value will have hundreds of digits, and we construct a special datatype to hold it. ****************************************************************/ BEGIN for each image I, BEGIN: Compute I's n-bin color histogram, H Normalize H to a standard number of pixels Convert H to an n-dimensional vector, V Call Hctol( V ) to print out position of V along a Hilbert curve traversal. Call Pctol( V ) to print out position of V along a Peano curve traversal. END Sort images along Hilbert curve positions: output sorted list Sort images along Peano curve positions: output sorted list /* These two orderings can then be used to generate Browser Windows */ END /*  * function: hilbert_cube_to_line --- Hctol  * Collapses m-dimensional coordinates to a line via  * Hilbert curve traversal  */ function Hctol( point ) { integer tau, /* chunk of digits of final line position */ lasttau;   /* holder for previous value of tau  */ integer i, j, k,  /* counters     */ lastbit=0, one, zero, r=0, b=0, DEGREE=19; /* DEGREE == number of bits precision per coordinate */ FOR i = DEGREE .. 1 {  /* Extract tau from all the coordinate values */  FOR j = 1 .. DIMENSION { SET j'th bit of tau = i'th bit of point[j] } SWAP tau and lasttau; XOR tau with lasttau; ROLL tau r bits to the left; tau->end = (tau->end + roller)%DIMENSION; /* twiddle bits in tau */ if( lastbit == 0 ) TOGGLE first bit of tau: replace 1 with 0, 0 with 1; TOGGLE b'th bit (from the left) of tau; /* Avalanche and send digits to the output buffer */ for( j = 1..DIMENSION ) { COMPUTE k = the XOR of the first j bits of tau; OUTPUT k; if(k==1) one=J; else zero=j; } /* compute b */ SET lastbit = k; if( lastbit == 0 ) b=DIMENSION-one; else b=DIMENSION-zero; COMPUTE r = (r+DIMENSION-1-b) % DIMENSION; } } /*  * function: peano_cube_to_line  * Collapses m-dimensional coordinates to a line via  * Peano curve traversal  */ void Pctol( point ) { int i, j, trit, flag=0, DEGREE=12; int modulus, h=0; int flags[2048]; /* flags are used to determine which way   the curve winds at any given point */ FOR all i, SET flags[i]=0; FOR i = DEGREE .. 1 FOR j = DIMENSION .. 1 { flag = flag XOR flags[j]; trit = i'th digit of point[j] /* This is in base 3, so trit takes on values 0, 1, or 2 */ if( trit==1 ) INVERT flag[j]; else if( flag is TRUE ) SET trit = 2-trit; /* Sets 2 to 0, or 0 to 2 */ OUTPUT trit; SET flag = flag XOR flags[j]; } } } /* Source code for collapsing points in a space to points in a line, via space-filling curve traversal. The “points” are arrays of plain old integer coordinates. These are combined to produce a single value, indicating a position along a line. Since we're dealing with high-dimensional spaces (DIMENSION == ˜600), the final value will have hundreds of digits, and we must construct a special datatype to hold it. */ #include <stdio.h> #define MAXD 700 #define DIMENSION length #define SIDELENGTH 531441 /*           */ /* Code to handle the numeral datatype */ /*           */ typedef struct numeral{ char d[MAXD]; /* At most MAXD digits    */ int end; /* position of highest digit */ } * numeral; char hopper[10]; int hopsize, hopcount, hopmod; /* Ninit --- allocates a numeral and sets its value to 0 */ numeral ninit( int length ) { numeral done; int i; if( (done=(numeral)malloc(sizeof(struct numeral)))==NULL ) { fprintf(stderr,“Numeral allocation problem.\n”); exit(1); } for(i=0; i<MAXD; i++) done−>d[i]=‘0’; done−>end=DIMENSION-1; return done; } /* Hopperhead --- initializes the “hopper.” More on this below */ void hopperhead( int size, int mod ) {  hopsize=size; hopmod=mod; hopcount=0; } /* Hop --- pours digits into a “hopper,” emptying the whole thing  *    when it becomes full.  *  * The hopper is a small buffer of digits in some small base b, such  * as 2 or 3. These digits are generated by the curve-mapping functions,  * in the form of very large binary/ternary numbers. We don't want to  * store all that in a file, so the hop function conglomerates N base-b  * digits into a single digit base N*b before printing it to standard out.  *  */ void hop( int c ) { int i,j; if(c !=0) hopper[hopcount++]=(char) c; if( hopcount >= hopsize || c==0 ){ /* Dump hopper in base-n format */ for(i=j=0; j<hopcount; j++) i = i*hopmod + hopper[j]-‘0’; printf(“%c”, i+‘’+1); hopcount=0; } } /*  * function: hilbert_cube_to_line  * Collapses m-dimensional coordinates to a line via  * Hilbert curve traversal  *  * This function accepts an array of integers (coordinates)  * and converts it to a position along a line, sending the  * digits to an output buffer which eventually prints to  * standard output.  */ void Hctol( int * v, int length ) { numeral tau, lasttau; int i, j, k, one, zero, lastbit = 0, lastbyte=0, roller=0, DEGREE=19; tau=ninit(DIMENSION); lasttau=ninit(DIMENSION); hopperhead(6,2); /* Set up hopper: Base 2, */ /* Buffer of six digits max */ for( i=1<<(DEGREE-1); i>0; i>>=1 ) { /* Extract tau from all the coordinate values */ for( j=0; j<DIMENSION; j++ ) tau−>d[j] = ((v[j]&i)==0) ? ‘0’ : ‘1’; tau−>end=DIMENSION-1; /* Perform: tau = lasttau; lasttau = tau; */ for(j=0; j<DIMENSION;j++ ) { k=tau−>d[j]; tau−>d[j] = ( lasttau−>d[j] & 1 ); lasttau−>d[j]=k; } /* Roll the value of tau “roller” bits to the left */ tau−>end = (tau−>end + roller)%DIMENSION; /* twiddle bits in tau */ if( lastbit==0 ) tau−>d[(tau−>end+1)%DIMENSION] = 1; tau−>d[(tau−>end+1+lastbyte)%DIMENSION] = 1; /* Avalanche and send digits to the output buffer */ for( j=k=0, one=zero=DIMENSION-1; j<DIMENSION; j++ ) { hop( tau−>d[(tau−>end+1+j)%DIMENSION] k ); k = ( tau−>d[(tau−>end+1+j)%DIMENSION] & 1 ); if(k) one=j; else zero=j; } /* compute lastbyte */ if( lastbit=k ) lastbyte=DIMENSION-1-zero; else lastbyte=DIMENSION-1-one; roller = (roller+DIMENSION-1-lastbyte) % DIMENSION; } hop(0);  /* flush the output buffer */ free(tau); free(lasttau); } /*  * function: peano_cube_to_line  * Collapses m-dimensional coordinates to a line via  * Peano curve traversal  *  * This function accepts an array of integers (coordinates)  * and converts it to a position along a line, sending the  * digits to an output buffer which eventually prints to  * standard output.  */ void Pctol( int * v, int length) { int i, j, trit, flag=0, DEGREE=12; int modulus, h=0; int flags[2048]; hopperhead(4,3); /* Set up hopper: base-3, */ /* Buffer of 4 digits max */ for(i=0; i<2048; i++) flags[i]=0; for( modulus=SIDELENGTH/3, i=DEGREE-1; i>=0; i--, modulus /= 3 ){ for( j=DIMENSION-1; j>=0; j-- ){ flag = flags[j]; /* XOR-out current flag */ trit = (v[j]/modulus)%3; /* Get current base-3 digit */ if( trit==1 ) flags[j] = 1; /* Invert current flag, or: */ else if( flag) trit = 2-trit; /* maybe invert the digit */ hop( trit+‘0’ ); /* Send digit to output */ flag = flags[j]; /* XOR-in the new flag value */ } } hop(0); /* Flush output buffer */ }

FIG. 1 shows one embodiment of a flowchart of the process of creating dual linearizations for each image. At step 101, a histogram H(Ij) is created of image Ij. One implementation of the histogram is by using color information. Other feature information like shape and size can also be implemented, and can be in the form of histogram or other feature representation.

Essentially, any image content that can be represented as a vector (a point in space) may be used. Points “close” to one another are similar in that particular kind of content. Once the set of images is converted into a set of points, the rest of the process is the same: traverse the “space” along the two curves, and output the data structure.

At step 102, the histogram of k attributes is mapped as a point p(j) in k-dimensional space, where k is any positive integer. At step 103, the position of p(j) is computed in a Hilbert traversal of the k-space. In one embodiment, the position of p(j) is computed along an interval [0,1] in a Hilbert traversal of the k-space. At step 104, the position of p(j) is computed along interval [0,1] in a Peano traversal of the k-space. At step 105, p(j)'s four nearest neighbors based on the values obtained from the Hilbert and Peano curve traversals are located. Their links are updated according to the positions of the images.

In one embodiment, a linked data structure is used to store each image as it is linearized. For example, a new image is placed into a first linked structure by its linearization based on the Hilbert curve traversal. The new image is linked to its nearest neighbor, i.e., the images closest to the new image in linearization. The new image is similarly placed into a second linked structure by its linearization based on the Peano curve traversal.

Once all the images are added to the data structure, it is possible to follow a first set of links to traverse the linearization based on the Hilbert traversal. It is also possible to follow a second set of links to traverse the linearization based on the Peano traversal. In another embodiment, linearizations based on other methods can be employed. Additionally more than two linearizations may be used.

One advantage of using the described data structure is that it does not need to be completely recalculated each time an image is added or deleted from the data structure. Instead, only a few links need to be modified for each addition or deletion of an image. A second advantage of the data structure is that it is data independent, so that two existing databases can be combined into one with little more than a simple list merge.

Clustering

Clustering is another method of providing a linearization to images. A cluster is a grouping of images using a tree-like structure. The nature of clusters is such that it is not unusual that like images are placed into the data structure at the same time. By performing a linearization based upon a smart traversal of the cluster, like images are more likely to be placed next to each other in the linearization.

One way of linearizing a cluster is to add an entire first group of images before traversing the cluster to add another group of images. For example, if a first group of images was all added to the cluster at the same time, these images should be linked together prior to moving on to a group of images that was added to the cluster at a different time than the first group.

In another embodiment, the attributes of the target image are compared with attributes of all groups of the cluster. The groups that are the closest in attributes are linearized first. Other groups are added based on their similarity in attributes. A centroid, or representative item having the average characteristics of an entire group, can be used to compare the target image with an entire group within the cluster.

Another way to perform a linearization is to traverse the cluster in an orderly fashion. For example, a first linearization can traverse the cluster taking all right branches prior to taking any left branches. A second linearization can traverse the cluster taking all left branches. Alternatively, a first linearization can alternate between right and left branches, and a second linearization can alternate in the opposite manner.

In another embodiment, multiple linearizations can be created by first performing a first traversal of a cluster by traversing a first random traversal of the cluster linearizing each of the images of a section before moving on to a different section. A second traversal of the entire cluster is then performed by a different random traversal of the cluster.

Display of Multi-linearizations

FIG. 4 shows an example of a browser interface based on a dual linearization. In this implementation, a total of 53 images are displayed at a time. The current image of focus, or the target image, is centered in the display. The images immediately to the left and right are the nearest neighbors 42 a in one linearization. The images immediately to the top and bottom are the nearest neighbors 42 b in a second linearization.

The nearest neighbors are the images closest to the target image in linearization. In the case of the space-filling curves, the nearest neighbors correspond to the points that are closest in position in a linearization to the point corresponding to the target image. In one embodiment, the first linearization may be based on a Hilbert curve, and the second linearization may be based on a Peano curve. In a second embodiment, the first linearization is based on a first traversal of a cluster data structure, and the second linearization is based on a second traversal of the cluster data structure.

In one embodiment, the rules of displaying nearest neighbors of an image are applied recursively to all other images. Additionally, in one embodiment, images further away from the center, or target image, are displayed with a smaller size to denote greater dissimilarity from those images closer to the center. In FIG. 3, the next nearest neighbors 43 to the target image are smaller than the nearest neighbors 42 a and 42 b. The next—next nearest neighbors 44 of the target image are even smaller then its next nearest neighbors 43.

FIG. 5 shows an example of a browser interface based on a triple linearization. In one embodiment, the images above and below an image are the nearest neighbors based on a first linearization. Two other images may correspond to nearest neighbors based on a second linearization, and the remaining two images may correspond to nearest neighbors based on a third linearization. In other embodiments, more linearizations may be added, and a mixture of linearizations based on different methods is possible.

FIG. 6 shows an example of a system implemented on an SQL server that communicates with a middleware server to construct and serve web pages to an end user. The SQL server maintains the data structure of the multiple linearizations. The web server allows easy selection of the target image from any of the displayed images. When one of the images is selected, it becomes the target image, and the nearest neighbor and next-nearest neighbor images are updated correspondingly.

Thus, a system and method for using multiple linearizations for image browsing is disclosed. The specific arrangements and methods described herein are merely illustrative of the principles of this invention. Numerous modifications in form and detail may be made without departing from the scope of the described invention. Although this invention has been shown in relation to a particular embodiment, it should not be considered so limited. Rather, the described invention is limited only by the scope of the appended claims.

The method steps of FIG. 1 may be performed by a computer processor executing instructions organized into a program module or a custom designed state machine. Storage devices suitable for tangibly embodying computer program instructions include all forms of non-volatile memory including, but not limited to: semiconductor memory devices such as EPROM, EEPROM, and flash devices; magnetic disks (fixed, floppy, and removable); other magnetic media such as tape; and optical media such as CD-ROM disks. Further, the methods described herein may be embodied in a hardware device such as a printed circuit board comprising discrete logic, integrated circuits, or specially designed application specific integrated circuits (ASIC). 

What is claimed is:
 1. A method comprising: determining n attributes for each of a first plurality of images, wherein n is an integer greater than one; providing a point in an n-dimensional space for each of the first plurality of images using the n attributes of each image; mapping the points for the first plurality of images to a curve; ordering the first plurality of images by traversing the curve; identifying a target image in the ordered first plurality of images; displaying the target image; identifying a second plurality of images that are close to the target image in the ordered first plurality of images; displaying the second plurality of images; selecting one of the second plurality of images as a new target image; displaying the new target image; and displaying the previous target image, which was the target image before selecting the new target image, wherein displaying the previous target image includes displaying the image with a size smaller than a size of the new target image.
 2. A method comprising: determining n attributes for each of a first plurality of images, wherein n is an integer greater than one; providing a point in an n-dimensional space for each of the first plurality of images using the n attributes of each image; mapping the points for the first plurality of images to a curve; ordering the first plurality of images by traversing the curve; and merging a second plurality of ordered images with the ordered first plurality of images, wherein merging the second plurality of ordered images further comprises adding a link to each of the second plurality of ordered images based upon a position in a data structure, that indicates a location on a second curve through multi-dimensional space.
 3. A method comprising: determining n attributes for each of a plurality of images, wherein n is an integer greater than one; providing a point in an n-dimensional space for each of the plurality of images using the n attributes of each image; adjusting a point for an image so that the point does not lie directly on a partition boundary for a curve and then mapping the points for the plurality of images to the curve; and ordering the plurality of images by traversing the curve.
 4. An instruction storage device having stored thereon data representing sequences of instructions that if executed cause a processor to perform operations comprising: determining n attributes for each of a plurality of images, wherein n is an integer greater than one; providing a point in an n-dimensional space for each of the plurality of images using the n attributes of each image; mapping the points for the plurality of images to a first space-filling curve and to a second space-filling curve that is different than the first space-filling curve; and ordering the plurality of images by traversing the curves.
 5. An instruction storage device having stored thereon data representing sequences of instructions that if executed cause a processor to perform operations comprising: determining n attributes for each of a first plurality of images, wherein n is an integer greater than one; providing a point in an n-dimensional space for each of the first plurality of images using the n attributes of each image; mapping the points for the first plurality of images to a curve; ordering the first plurality of images by traversing the curve; identifying a target image; displaying the target image; identifying a second plurality of images that are close to the target image in the ordered first plurality of images; displaying the second plurality of images; selecting one of the second plurality of images as a new target image; displaying the new target image; and displaying the previous target image, which was the target image before selecting the new target image, wherein displaying the previous target image includes displaying the image with a size smaller than a size of the new target image.
 6. A method comprising: adding a first set of images to a tree-like cluster including a plurality of branches; determining a first linearization of the first set of images by traversing the cluster; adding a second set of images to the cluster; determining a second linearization of the second set of images; and ordering the first and second sets of images using the first linearization and the second linearization.
 7. The method of claim 6, wherein adding a first set of images comprises adding a first set of images that are alike in at least one attribute.
 8. The method of claim 6, wherein determining the first linearization comprises using a consistent strategy for traversing the cluster.
 9. The method of claim 1, wherein the curve comprises a space-filling curve.
 10. The method of claim 1, further comprising browsing for a desired image by scrolling through the displayed images.
 11. The method of claim 1, further comprising: mapping the points for the first plurality of images to a second curve; ordering the first plurality of images a second time by traversing the second curve; identifying a third plurality of images that are close to the target image in the second ordering of the first plurality of images; and displaying the third plurality of images.
 12. A data structure on a computer-readable storage device having the order of the first plurality of images created by the method of claim
 1. 13. The method of claim 2, wherein the curve comprises a space-filling curve.
 14. The method of claim 13, wherein the second plurality of ordered images are ordered with the space-filling curve.
 15. The method of claim 2, wherein said merging comprises adding a link from an image of the second plurality of ordered images to a nearest neighbor of the first plurality of ordered images to a data structure.
 16. A data structure on a computer-readable storage device having the order of the first and the second pluralities of images created by the method of claim
 2. 17. The method of claim 3, wherein the curve comprises a space-filling curve.
 18. The method of claim 17, wherein the space-filling curve comprises a Hilbert curve.
 19. The method of claim 17, wherein the space-filling curve comprises a Peano curve.
 20. The method of claim 3, wherein the curve comprises a curve having a property that points that are close together on the curve are close together in the n-dimensional space.
 21. The method of claim 3, further comprising: mapping the points for the plurality of images to a second curve; and ordering the plurality of images a second time by traversing the second curve.
 22. The method of claim 21, wherein the curve comprises first space-filling curve and the second curve comprises a second different space-filling curve.
 23. The method of claim 3, further comprising merging a second plurality of images that are ordered based on the curve with the ordered plurality of images.
 24. The method of claim 3, further comprising displaying a second plurality of images of the ordered plurality of images, the second plurality of images including a target image and at least one image that is close to the target image in the ordered plurality of images.
 25. A data structure on a computer-readable storage device having the order of the plurality of images created by the method of claim
 3. 26. The instruction storage device of claim 4, wherein the instructions to perform operations comprising determining n attributes further comprise instructions that if executed cause the processor to perform operations comprising determining a number of members in a color bin of a histogram.
 27. The instruction storage device of claim 4, wherein the instructions to perform operations comprising mapping further comprise instructions that if executed cause the processor to perform operations comprising mapping the points for the plurality of images to a Hilbert curve and a Peano curve.
 28. The instruction storage device of claim 4, wherein the instructions further comprise instructions that if executed cause the processor to perform operations comprising displaying a second plurality of images of the ordered plurality of images, the second plurality of images including a target image and at least one image that is close to the target image in the ordered plurality of images.
 29. The instruction storage device of claim 4, wherein the instructions to perform operations comprising ordering further comprise instructions that if executed cause the processor to perform operations comprising ordering the plurality of images once based on the first space filling curve and again based on the second space filling curve.
 30. The instruction storage device of claim 4, wherein the instructions further comprise instructions that if executed cause the processor to perform operations comprising merging an image with the ordered plurality of images by adding a link from the image to a nearest neighbor of the ordered plurality of images to a data structure.
 31. The instruction storage device of claim 4, wherein the instructions further comprise instructions that if executed cause the processor to perform operations comprising merging a second plurality of images that are ordered based on the first space-filling curve with the ordered plurality of images.
 32. The instruction storage device of claim 5, wherein the instructions to perform operations comprising mapping further comprise instructions that if executed cause the processor to perform operations comprising mapping the points for the first plurality of images to a space-filling curve.
 33. The instruction storage device of claim 5, wherein the instructions to perform operations comprising determining the n attributes further comprise instructions that if executed cause the processor to perform operations comprising determining a number of members in a color bin of a histogram.
 34. The method of claim 6, further comprising displaying a plurality of images based on the ordering of the sets of images.
 35. A data structure on a computer-readable storage device having the order of the first and the second sets of images created by the method of claim
 6. 36. A method comprising: determining n attributes for each of a plurality of images, wherein n is an integer greater than one; providing a point in an n-dimensional space for each of the plurality of images using the n attributes of each image; mapping the points for the plurality of images to a first space-filling curve and to a second space-filling curve that is different than the first space-filling curve; and ordering the plurality of images by traversing the curves.
 37. The method of claim 36, wherein said determining the n attributes comprises determining a number of members in a color bin of a histogram.
 38. The method of claim 36, wherein said mapping comprises mapping the points for the plurality of images to a Hilbert curve and a Peano curve.
 39. The method of claim 36, further comprising displaying a second plurality of images of the ordered plurality of images, the second plurality of images including a target image and at least one image that is close to the target image in the ordered plurality of images.
 40. The method of claim 36, wherein said ordering comprises ordering the plurality of images once based on the first space filling curve and again based on the second space filling curve.
 41. The method of claim 36, further comprising merging an image with the ordered plurality of images by adding a link from the image to a nearest neighbor of the ordered plurality of images to a data structure.
 42. The method of claim 36, further comprising merging a second plurality of images that are ordered based on the first space-filling curve with the ordered plurality of images. 